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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 7 hours ago
00061514 Vacancy ID P020571 Full-time/Part-time Permanent/Time-Limited Full-Time Permanent If time-limited, estimated duration of appointment Hours per week 40 Work Schedule Monday – Friday, 8:00 am – 5:00
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-armed Bandits, Bayesian Optimization. Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization. Computer Networking: Resource-Constrained Networking (e.g., Internet
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Centre (NCN). The Principal Investigator is Dr. Eng. Piotr Kopka, email: Piotr.Kopka@ncbj.gov.pl Project description: The project aims to develop a new class of inverse Bayesian models called STE-EU-SCALE
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for the eight-year project, developing software and maintain hardware such as computer, storage systems and scientific equipment for the collection and compilation, analysis, version control and publication
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design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in serving in protocol review
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QUANTITATIVE METHODS and is part of a cluster hire across the School of Social Sciences. The specialty area should be in human factors/human-computer interaction (HF/HCI), industrial-organizational psychology
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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(for example, R, Python, or Matlab). Experience with graph modeling, Bayesian statistics, or causal inference is a plus. The candidate will join an integrated team of computational scientists, molecular
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational